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and the Faculty of Physics. We are seeking a highly qualified researcher with a strong background in tensor network methods, in particular the development of numerical tensor network algorithms, and
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one of the following areas: - Methodology development in wavefunction-based electronic structure methods, quantum Monte Carlo, tensor networks, or quantum embedding methods
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at Cordell Health Limited’s premises in Portsmouth. Requirements Data Analytics & Machine Learning: including model development using Pandas, NumPy, Scikit-learn, or Tensor flow Programming Skills: Python
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on theoretical and computational aspects of quantum many-body systems, including Tensor Networks, Neural Quantum States, Stabilizer formalism, Complexity measures such as entanglement and quantum magic, quantum
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, fermionic and bosonic quantum codes, machine learning approaches to decoding, magic and nonstabilizerness in quantum codes, tensor-network methods for code construction and decoding, and resource estimation
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tensors and local plastic strains (including operating slip systems) from Lab-4DµXRD data, in collaboration with the DTU Lab4DMade team and the Danish company Xnovo Technology ApS. Performing in situ Lab
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algebraic complexity, with emphasis on GCT, border complexity, Waring/tensor rank, and connections to representation theory and algebraic geometry. Publish research outcomes in leading venues in theoretical
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nanostructural information from SAXS tensor tomography Investigate how mineralized biological tissues, including bone, adapt their nanostructure to different mechanical and functional demands Help to design and
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computation, probabilistic machine learning, latent-variable models, unsupervised learning, or matrix and tensor factorization is an advantage. Experience with computational methods for large or high
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quantum magnetism and strongly correlated systems, as well as classical methods such as exact diagonalization, tensor networks or DMRG, and quantum Monte Carlo. Familiarity with inelastic neutron scattering